Behavior Alignment as a Mechanism for Interaction Belief Matching

When two or more agents have to perform an interaction that is part of a business process instance (BPI) they can either follow an established protocol, or use their own experience and judgment. In the second case, there is no protocol enacted, or if it is, it does not cover special circumstances that occur in this particular instance. In this case, the agents have to use their own experience, acquired in previous and similar kind of interactions. Before the interaction, the agents will build an intended behavior (their own course of action) and also will presume what the other agents are doing (expected behavior). The intended behaviors of the agents interacting are not always matching, in this case the interaction will not be completed successfully. The process of collaboratively changing behavior for successful matching is called alignment. In this thesis, a formalism will be presented to define agents' behaviors (as exhibited in agent to agent interactions), by an extension of Petri Nets. Secondly, it will be shown how behaviors of different agents can be aligned using specific alignment policies. A mechanism using a neural network is proposed for automatic choosing of an alignment policy by the agent. Furthermore, a method how agents can overcome their lack of experience by escaping is described. This research will make the system more reliable, and to reduce the necessary human intervention. Possible future directions of research are also pointed out.